Optimizing Data Pipelines for High-Performance Machine Learning in AWS
DOI:
https://doi.org/10.53555/ephijse.v10i1.300Keywords:
AWS Machine Learning, Data pipelines, Model training optimization, High-performance computing, Big Data in AWSAbstract
Strong and efficient data pipelines enable rapid data collecting, preparation, and transformation thereby defining the applications of high-performance machine learning. Improving data pipelines in the present computing environment will enhance the opportunities of machine learning models. This work demonstrates how precisely leveraging AWS resources in machine learning applications could significantly increase the data pipeline building and deployment efficiency. AWS offers several tools, including S3 for scalable storage, Lambda for serverless computing, Glue for data integration, and SageMaker for machine learning model building and deployment. Engineers and data analysts might design tailored pipelines with these technologies allowing real-time analysis and data management automation. Including unstructured as well as organized input into machine learning models improves model efficiency and accuracy. Notwithstanding the benefits, improving data pipelines in AWS brings difficulties including higher data latency, guaranteed seamless service compatibility, and low cost related to major data throughput. Tight monitoring, continuous performance enhancement, and effective error control are needed for an increasingly more challenging pipeline management approach. Dealing with these challenges calls on a strong awareness of AWS architectural concepts and machine learning requirements. Among other benefits, a competent ML pipeline built on AWS provides shorter processing times, improved scalability, and higher data security. It enables rapid experimentation, iterative development, and effective resource use. The article examines important elements of pipeline optimization, provides suggestions on best practices, creative concepts, and practical solutions helping businesses to make good use of AWS machine learning. In a data-centric society, the focus is on the main influence of simplified data pipelines in enabling business innovation and increasing machine learning activities. Through continuous pipeline development, organizations could maintain a competitive edge in rapid developing digital industries.
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